{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第 14 章 生成式对抗网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 14.2 搭建生成式对抗网络"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14.2.1 生成器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 256)               25856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 784)               803600    \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 28, 28, 1)         0         \n",
      "=================================================================\n",
      "Total params: 1,493,520\n",
      "Trainable params: 1,489,936\n",
      "Non-trainable params: 3,584\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "\n",
    "L = keras.layers\n",
    "\n",
    "LATENT_DIM = 100 # 潜在空间维度\n",
    "IMAGE_SHAPE = (28, 28, 1) # 输出图像尺寸\n",
    "\n",
    "generator_net = [\n",
    "    L.Input(shape=(LATENT_DIM, )),\n",
    "    L.Dense(256),\n",
    "    L.LeakyReLU(alpha=0.2),\n",
    "    L.BatchNormalization(momentum=0.8),\n",
    "    L.Dense(512),\n",
    "    L.LeakyReLU(alpha=0.2),\n",
    "    L.BatchNormalization(momentum=0.8),\n",
    "    L.Dense(1024),\n",
    "    L.LeakyReLU(alpha=0.2),\n",
    "    L.BatchNormalization(momentum=0.8),\n",
    "    L.Dense(np.prod(IMAGE_SHAPE), activation='tanh'),\n",
    "    L.Reshape(IMAGE_SHAPE),\n",
    "]\n",
    "\n",
    "generator = keras.models.Sequential(generator_net)\n",
    "generator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 14.2.2 判别器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 533,505\n",
      "Trainable params: 533,505\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 判别器模型的层列表\n",
    "discriminator_net = [\n",
    "    L.Input(shape=IMAGE_SHAPE),\n",
    "    L.Flatten(),\n",
    "    L.Dense(512),\n",
    "    L.LeakyReLU(alpha=0.2),\n",
    "    L.Dense(256),\n",
    "    L.LeakyReLU(alpha=0.2),\n",
    "    L.Dense(1, activation='sigmoid'),\n",
    "]\n",
    "\n",
    "optimizer = keras.optimizers.Adam(0.0002, 0.5)\n",
    "\n",
    "discriminator = keras.models.Sequential(discriminator_net)\n",
    "discriminator.compile(loss='binary_crossentropy',\n",
    "                      optimizer=optimizer,\n",
    "                      metrics=['accuracy'])\n",
    "discriminator.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "### 14.2.3 对抗模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense (Dense)                (None, 256)               25856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu (LeakyReLU)      (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 256)               1024      \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               131584    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 512)               2048      \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1024)              525312    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 784)               803600    \n",
      "_________________________________________________________________\n",
      "reshape (Reshape)            (None, 28, 28, 1)         0         \n",
      "_________________________________________________________________\n",
      "input_2 (InputLayer)         multiple                  0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 784)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 512)               401920    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 1)                 257       \n",
      "=================================================================\n",
      "Total params: 2,027,025\n",
      "Trainable params: 1,489,936\n",
      "Non-trainable params: 537,089\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 对抗模型使用生成器模型层和判别器模型层，它们共享权重\n",
    "adversarial_net = generator_net + discriminator_net\n",
    "\n",
    "# 冻结判别器的权重\n",
    "# trainable 属性只有编译后生效，所以之前的判别器模型同样的层还是可以训练的\n",
    "for layer in discriminator_net:\n",
    "    layer.trainable = False\n",
    "\n",
    "adversarial = keras.models.Sequential(adversarial_net)\n",
    "\n",
    "# 编译对抗模型\n",
    "optimizer = keras.optimizers.Adam(0.0002, 0.5)\n",
    "adversarial.compile(loss='binary_crossentropy',\n",
    "                    optimizer=optimizer,\n",
    "                    metrics=['accuracy'])\n",
    "adversarial.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import clear_output\n",
    "\n",
    "def sample_images():\n",
    "    rows, columns = 3, 10\n",
    "    sample_count = rows * columns\n",
    "\n",
    "    plt.figure(figsize=(columns, rows))\n",
    "\n",
    "    # 使用生成器生成图像\n",
    "    noise = np.random.normal(0, 1, (sample_count, LATENT_DIM))\n",
    "    gen_imgs = generator.predict(noise)\n",
    "    # 生成器图像张量范围从 [-1, 1] 改到 [0, 1]\n",
    "    gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "\n",
    "    index = 0\n",
    "    for row in range(rows):\n",
    "        for col in range(columns):\n",
    "            image = np.reshape(gen_imgs[index], [28, 28])\n",
    "            plt.subplot(rows, columns, index+1)\n",
    "            plt.imshow(image, cmap='gray')\n",
    "            plt.axis('off')\n",
    "            index += 1\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    return gen_imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tqdm\n",
    "\n",
    "def train(epochs=20, batch_size=64):\n",
    "    training_samples = []\n",
    "    training_history = []\n",
    "\n",
    "    # 读取数据集，我们只需要图像数据，不需要标签和测试数据。\n",
    "    (image_set, _), (_, _) = keras.datasets.mnist.load_data()\n",
    "\n",
    "    # 数据归一化\n",
    "    image_set = image_set / 127.5 - 1.\n",
    "    # 数据格式转换 [count, 28, 28] -> [count, 28, 28, 1]\n",
    "    image_set = image_set.reshape(len(image_set), 28, 28, 1)\n",
    "\n",
    "    # 准备 batch_size 大小的真假数据标签\n",
    "    valid = np.ones((batch_size))\n",
    "    fake = np.zeros((batch_size))\n",
    "\n",
    "    batch_count = int(len(image_set) / batch_size)\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        print(f\"Epoch {epoch}/{epochs}\")\n",
    "        for batch in tqdm.trange(batch_count):\n",
    "            # ------ 生成器生成图像 ------\n",
    "            # 随机选择 batch_size 数量的数据作为训练数据\n",
    "            noise = np.random.uniform(-1, 1, (batch_size, LATENT_DIM))\n",
    "            imgs = image_set[batch * batch_size:(batch + 1) * batch_size]\n",
    "\n",
    "            # 生成噪音数据作为生成器输入\n",
    "            noise = np.random.uniform(-1, 1, (batch_size, LATENT_DIM))\n",
    "\n",
    "            # 使用生成器生成生成图像\n",
    "            gen_imgs = generator.predict(noise)\n",
    "\n",
    "            # ------ 训练判别器 ------\n",
    "            # 使用真实图像和生成图像训练判别器，真实图像标签全部为 1，生成图像标签全部为 0\n",
    "            d_state_real = discriminator.train_on_batch(imgs, valid)\n",
    "            d_state_fake = discriminator.train_on_batch(gen_imgs, fake)\n",
    "            d_state = 0.5 * np.add(d_state_real, d_state_fake)\n",
    "\n",
    "            # ------ 训练生成器 ------\n",
    "            # 生成噪音数据作为对抗模型\n",
    "            noise = np.random.uniform(-1, 1, (batch_size, LATENT_DIM))\n",
    "\n",
    "            # 训练对抗模型，目标是生成判别器认为真实图像的图像，所以标签为 1\n",
    "            # 由于对抗模型中的判别器的层都冻结了，所以实际上在训练生成器，不断生成更加逼真的图像\n",
    "            adv_state = adversarial.train_on_batch(noise, valid)\n",
    "\n",
    "            training_history.append([*d_state, *adv_state])\n",
    "            # 更新进度条后缀，用于输出训练进度\n",
    "#             state = f\"[D loss: {d_state[0]:.4f} acc: {d_state[1]:.4f}] \" \\\n",
    "#                     f\"[A loss: {adv_state[0]:.4f} acc: {adv_state[1]:.4f}\"\n",
    "#             print(state)\n",
    "            \n",
    "        # 清空 cell 之前的输出\n",
    "        clear_output(wait=True)\n",
    "        samples = sample_images()\n",
    "        training_samples.append((batch, samples))\n",
    "\n",
    "    return training_history, training_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 30 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 2/937 [00:00<01:03, 14.80it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|██▎       | 220/937 [00:15<00:50, 14.12it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-21-df25d9dc7319>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# 调用 train 函数就可以开始训练了\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msamples\u001b[0m  \u001b[0;34m=\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-20-e4c03fc57fa9>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(epochs, batch_size)\u001b[0m\n\u001b[1;32m     45\u001b[0m             \u001b[0;31m# 训练对抗模型，目标是生成判别器认为真实图像的图像，所以标签为 1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;31m# 由于对抗模型中的判别器的层都冻结了，所以实际上在训练生成器，不断生成更加逼真的图像\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m             \u001b[0madv_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0madversarial\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnoise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m             \u001b[0mtraining_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0md_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0madv_state\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(self, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m    971\u001b[0m       outputs = training_v2_utils.train_on_batch(\n\u001b[1;32m    972\u001b[0m           \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 973\u001b[0;31m           class_weight=class_weight, reset_metrics=reset_metrics)\n\u001b[0m\u001b[1;32m    974\u001b[0m       outputs = (outputs['total_loss'] + outputs['output_losses'] +\n\u001b[1;32m    975\u001b[0m                  outputs['metrics'])\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(model, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[1;32m    262\u001b[0m       \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    263\u001b[0m       \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 264\u001b[0;31m       output_loss_metrics=model._output_loss_metrics)\n\u001b[0m\u001b[1;32m    265\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    266\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mreset_metrics\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[0;34m(model, inputs, targets, sample_weights, output_loss_metrics)\u001b[0m\n\u001b[1;32m    309\u001b[0m           \u001b[0msample_weights\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weights\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    310\u001b[0m           \u001b[0mtraining\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 311\u001b[0;31m           output_loss_metrics=output_loss_metrics))\n\u001b[0m\u001b[1;32m    312\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m     \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_eager.py\u001b[0m in \u001b[0;36m_process_single_batch\u001b[0;34m(model, inputs, targets, output_loss_metrics, sample_weights, training)\u001b[0m\n\u001b[1;32m    266\u001b[0m           \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backwards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaled_total_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 268\u001b[0;31m           \u001b[0mgrads\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscaled_total_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainable_weights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    269\u001b[0m           if isinstance(model.optimizer,\n\u001b[1;32m    270\u001b[0m                         loss_scale_optimizer.LossScaleOptimizer):\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/backprop.py\u001b[0m in \u001b[0;36mgradient\u001b[0;34m(self, target, sources, output_gradients, unconnected_gradients)\u001b[0m\n\u001b[1;32m   1012\u001b[0m         \u001b[0moutput_gradients\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m         \u001b[0msources_raw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mflat_sources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m         unconnected_gradients=unconnected_gradients)\n\u001b[0m\u001b[1;32m   1015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_persistent\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/imperative_grad.py\u001b[0m in \u001b[0;36mimperative_grad\u001b[0;34m(tape, target, sources, output_gradients, sources_raw, unconnected_gradients)\u001b[0m\n\u001b[1;32m     74\u001b[0m       \u001b[0moutput_gradients\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m       \u001b[0msources_raw\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m       compat.as_str(unconnected_gradients.value))\n\u001b[0m",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/eager/backprop.py\u001b[0m in \u001b[0;36m_gradient_function\u001b[0;34m(op_name, attr_tuple, num_inputs, inputs, outputs, out_grads, skip_input_indices)\u001b[0m\n\u001b[1;32m    136\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mnum_inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mgrad_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmock_op\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mout_grads\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    140\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/ops/math_grad.py\u001b[0m in \u001b[0;36m_DivNoNanGrad\u001b[0;34m(op, grad)\u001b[0m\n\u001b[1;32m   1305\u001b[0m   \u001b[0msy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1306\u001b[0m   \u001b[0mrx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgen_array_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast_gradient_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1307\u001b[0;31m   \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1308\u001b[0m   \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1309\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward_compatible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2019\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m14\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/util/dispatch.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;34m\"\"\"Call target, and fall back on dispatchers if there is a TypeError.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    179\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 180\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    181\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    182\u001b[0m       \u001b[0;31m# Note: convert_to_eager_tensor currently raises a ValueError, not a\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/ops/math_ops.py\u001b[0m in \u001b[0;36mconj\u001b[0;34m(x, name)\u001b[0m\n\u001b[1;32m   3454\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3455\u001b[0m     \u001b[0mdt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3456\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_floating\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3457\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3458\u001b[0m   \u001b[0;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Conj\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/tensorflow_core/python/framework/dtypes.py\u001b[0m in \u001b[0;36mis_floating\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    146\u001b[0m     \u001b[0;34m\"\"\"Returns whether this is a (non-quantized, real) floating point type.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    147\u001b[0m     return ((self.is_numpy_compatible and\n\u001b[0;32m--> 148\u001b[0;31m              np.issubdtype(self.as_numpy_dtype, np.floating)) or\n\u001b[0m\u001b[1;32m    149\u001b[0m             self.base_dtype == bfloat16)\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Desktop/book/intro_to_tf2.0_code/venv/lib/python3.7/site-packages/numpy/core/numerictypes.py\u001b[0m in \u001b[0;36missubdtype\u001b[0;34m(arg1, arg2)\u001b[0m\n\u001b[1;32m    363\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    364\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 365\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0mset_module\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'numpy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    366\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0missubdtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    367\u001b[0m     \"\"\"\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# 调用 train 函数就可以开始训练了\n",
    "history, samples  = train(epochs=20, batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 可视化训练过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.dpi'] = 180\n",
    "\n",
    "def visualize_samples(train_samples):\n",
    "    nrows, ncols = 3, 5\n",
    "    fig, axs = plt.subplots(nrows=nrows, ncols=ncols, figsize=(ncols*4, nrows*4))\n",
    "    \n",
    "    for index, ax in enumerate(axs.flatten()):\n",
    "        ax.axis('off')\n",
    "        sample_index = int(len(train_samples) / (nrows * ncols)) * index\n",
    "    \n",
    "        if sample_index < len(train_samples):\n",
    "            batch, images = train_samples[sample_index]\n",
    "            row0 = np.concatenate((images[0], images[1], images[2]), axis=1)\n",
    "            row1 = np.concatenate((images[3], images[4], images[5]), axis=1)\n",
    "            row2 = np.concatenate((images[6], images[7], images[8]), axis=1)\n",
    "            image = np.concatenate((row0, row1, row2))\n",
    "            ax.imshow(image.squeeze(), cmap='gray')\n",
    "            ax.set_title(batch)        \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x2160 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_samples(samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
